New energy automobile reliability evaluation method based on Internet of Vehicles data

Abstract

The embodiment of the invention relates to the technical field of Internet of Vehicles big data analysis, and discloses an Internet of Vehicles data-based new energy vehicle reliability evaluation method, which comprises the steps of obtaining Internet of Vehicles data of each new energy vehicle, and obtaining historical charging fragment data and historical driving fragment data of a target vehicle type based on the Internet of Vehicles data; based on the historical charging fragment data, calculating a battery system reliability score of the target vehicle type; based on the historical driving fragment data, calculating a driving system reliability score of the target vehicle type; based on the historical charging fragment data and the historical driving fragment data, calculating an electrical system reliability score of the target vehicle type; and evaluating the reliability of the target vehicle model based on the battery system reliability score, the driving system reliability score and the electrical system reliability score. By applying the technical scheme of the invention, the rationality and accuracy of reliability evaluation of the target vehicle model can be improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of big data analysis of the Internet of Vehicles, and specifically to a method for evaluating the reliability of new energy vehicles based on Internet of Vehicles data. Background Art

[0002] When evaluating the reliability of new energy vehicles in related technologies, the reliability is usually evaluated based on the fault problems indicated by the maintenance data in the life cycle of new energy vehicles. However, since the logic for triggering faults in each vehicle enterprise is user-defined and there are differences in the definition of the fault triggering threshold conditions and durations, the reliability evaluation criteria for each vehicle model are different and there are rationality problems. Summary of the Invention

[0003] In view of the above problems, the embodiments of the present invention provide a method for evaluating the reliability of new energy vehicles based on Internet of Vehicles data, which is used to solve the problems of different reliability evaluation criteria for new energy vehicles and rationality in related technologies.

[0004] According to one aspect of the embodiments of the present invention, a method for evaluating the reliability of new energy vehicles based on Internet of Vehicles data is provided. The method includes: obtaining the Internet of Vehicles data of each new energy vehicle, and obtaining the historical charging segment data and historical driving segment data of the target vehicle model based on the Internet of Vehicles data; calculating the reliability score of the battery system of the target vehicle model based on the historical charging segment data; calculating the reliability score of the drive system of the target vehicle model based on the historical driving segment data; calculating the reliability score of the electrical system of the target vehicle model based on the historical charging segment data and the historical driving segment data; and evaluating the reliability of the target vehicle model based on the reliability scores of the battery system, the drive system, and the electrical system.

[0005] In an alternative embodiment, calculating the reliability score of the battery system of the target vehicle model based on the historical charging segment data includes:

[0006] Calculating the maximum-minimum temperature difference of each cell and the state of charge error of each new energy vehicle based on the historical charging segment data;

[0007] Calculating the temperature difference score corresponding to the maximum-minimum temperature difference and the charge error score corresponding to the state of charge error based on the box plot;

[0008] Calculating the reliability score of the battery system of the target vehicle model based on the temperature difference score and the charge error score.

[0009] In an alternative embodiment, calculating the maximum-minimum temperature difference of each cell and the state of charge error of each new energy vehicle based on the historical charging segment data includes:

[0010] Perform feature analysis on historical charging segment data to obtain the charging temperature characteristics and charging attribute characteristics corresponding to each historical charging segment;

[0011] Based on the maximum average temperature and minimum average temperature in the charging temperature characteristics, calculate the cell temperature range of each new energy vehicle;

[0012] Based on the average charging power, rated power, and battery health status in the charging attribute characteristics, calculate the average charge state of each new energy vehicle for each segment;

[0013] Based on the difference between the average charge state of each segment and the charge state of the corresponding segment, calculate the charge state error of each new energy vehicle.

[0014] In an alternative embodiment, calculating the battery system reliability score for the target vehicle type based on historical charging segment data further includes:

[0015] Based on historical charging segment data, calculate the first cell temperature range for each new energy vehicle within the first calculation period and the first charge state period error;

[0016] Based on the box plot, calculate the first temperature range score corresponding to the first cell temperature range and the first charge error period score corresponding to the first charge state period error;

[0017] Based on historical charging segment data and the first charge error period score, calculate the second temperature range score and the second charge error period score for each new energy vehicle within the second calculation period;

[0018] Based on the first temperature range score, the first charge error period score, the second temperature range score, and the second charge error period score, calculate the battery system reliability score for the target vehicle type.

[0019] In an alternative embodiment, calculating the drive system reliability score for the target vehicle type based on historical driving segment data includes:

[0020] Based on historical driving segment data, obtain the drive motor temperature and motor controller temperature of each new energy vehicle at the target vehicle speed;

[0021] Count the proportion of the first vehicles in which the drive motor temperature is greater than the motor temperature threshold within the third calculation period, and the proportion of the second vehicles in which the motor controller temperature is greater than the controller temperature threshold;

[0022] Based on the box plot, calculate the motor temperature score corresponding to the proportion of the first vehicles and the controller temperature score corresponding to the proportion of the second vehicles;

[0023] Based on the motor temperature score and the controller temperature score, calculate the drive system reliability score for the target vehicle type.

[0024] In an alternative embodiment, calculating the proportion of the first vehicles in which the temperature of the drive motor is greater than the motor temperature threshold and the proportion of the second vehicles in which the temperature of the motor controller is greater than the controller temperature threshold during the third calculation period includes:

[0025] Calculating the first consecutive trigger count in which the temperature of the drive motor is greater than the motor temperature threshold and the second consecutive trigger count in which the temperature of the motor controller is greater than the controller temperature threshold during the third calculation period;

[0026] Obtaining the proportion of new energy vehicles in which the first consecutive trigger count is greater than the first trigger count threshold to obtain the proportion of the first vehicles;

[0027] Obtaining the proportion of new energy vehicles in which the second consecutive trigger count is greater than the second trigger count threshold to obtain the proportion of the second vehicles.

[0028] In an alternative embodiment, calculating the reliability score of the electrical system of the target model based on historical charging segment data and historical driving segment data includes:

[0029] Based on the historical charging segment data and historical driving segment data, obtaining the insulation resistance value of each new energy vehicle during the fourth calculation period;

[0030] Calculating the proportion of the third vehicles in which the insulation resistance value is less than the insulation trigger threshold during the fourth calculation period;

[0031] Calculating the reliability score of the electrical system corresponding to the proportion of the third vehicles based on the box plot.

[0032] In an alternative embodiment, calculating the proportion of the third vehicles in which the insulation resistance value is less than the insulation trigger threshold during the fourth calculation period includes:

[0033] Calculating the third consecutive trigger count in which the insulation resistance value is less than the insulation trigger threshold during the fourth calculation period;

[0034] Obtaining the proportion of new energy vehicles in which the third consecutive trigger count is greater than the third trigger count threshold to obtain the proportion of the third vehicles.

[0035] In an alternative embodiment, evaluating the reliability of the target model based on the battery system reliability score, the drive system reliability score, and the electrical system reliability score includes:

[0036] Based on the vehicle networking data, obtaining the historical alarm data of the target model;

[0037] Based on the historical alarm data, counting the battery alarm count of the battery system, the drive alarm count of the drive system, and the electrical alarm count of the electrical system;

[0038] Normalize the number of battery alarms, drive alarms, and electrical alarms to obtain a normalization result;

[0039] Evaluate the reliability of the target vehicle model based on the normalization result, battery system reliability score, drive system reliability score, and electrical system reliability score.

[0040] In an alternative embodiment, historical charging segment data and historical driving segment data of the target vehicle model are obtained based on vehicle networking data, including:

[0041] Perform data analysis on the vehicle networking data to obtain the vehicle status signal of the target vehicle model;

[0042] Based on the vehicle status signal, determine the charging status of each new energy vehicle in the target vehicle model, where the charging status includes a parked charging status and a non-parked charging status;

[0043] Based on the adjacent relationship between the parked charging status and the non-parked charging status, obtain the historical charging segment data and historical driving segment data of the target vehicle model.

[0044] According to another aspect of the embodiments of the present invention, a new energy vehicle reliability evaluation device based on vehicle networking data is provided, including: a data acquisition module for acquiring the vehicle networking data of each new energy vehicle and obtaining the historical charging segment data and historical driving segment data of the target vehicle model based on the vehicle networking data; a battery scoring module for calculating the battery system reliability score of the target vehicle model based on the historical charging segment data; a drive scoring module for calculating the drive system reliability score of the target vehicle model based on the historical driving segment data; an electrical scoring module for calculating the electrical system reliability score of the target vehicle model based on the historical charging segment data and historical driving segment data; and a vehicle model evaluation module for evaluating the reliability of the target vehicle model based on the battery system reliability score, drive system reliability score, and electrical system reliability score.

[0045] According to another aspect of the embodiments of the present invention, a vehicle is provided, including: a processor, a memory, a communication interface, and a communication bus. The processor, memory, and communication interface complete communication with each other through the communication bus; the memory is used to store at least one executable instruction, and the executable instruction causes the processor to execute the operations of the foregoing new energy vehicle reliability evaluation method based on vehicle networking data.

[0046] According to yet another aspect of the embodiments of the present invention, a computer-readable storage medium is provided, and at least one executable instruction is stored in the storage medium. The executable instruction causes the vehicle / device to execute the operations of the foregoing new energy vehicle reliability evaluation method based on vehicle networking data.

[0047] In one aspect according to an embodiment of the present invention, there is provided a computer program product including computer instructions for causing a computer to execute the method for evaluating the reliability of a new energy vehicle based on vehicle networking data in the above first aspect or any corresponding embodiment thereof.

[0048] The technical solution provided by the embodiment of the present invention obtains the vehicle networking data of each new energy vehicle, and based on the vehicle networking data, obtains the historical charging segment data and historical driving segment data of the target model; calculates the reliability score of the battery system of the target model based on the historical charging segment data; calculates the reliability score of the drive system of the target model based on the historical driving segment data; calculates the reliability score of the electrical system of the target model based on the historical charging segment data and historical driving segment data; and evaluates the reliability of the target model based on the reliability scores of the battery system, drive system, and electrical system, thereby improving the rationality and accuracy of the evaluation of the reliability of new energy vehicles.

[0049] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to be able to understand the technical means of the embodiment of the present invention more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features, and advantages of the embodiment of the present invention more obvious and understandable, the following specifically describes the specific implementation manners of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The drawings are only used to illustrate the embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0051] Figure 1 A schematic flowchart of a method for evaluating the reliability of a new energy vehicle based on vehicle networking data provided by the present invention is shown;

[0052] Figure 2 A first scoring schematic diagram of a method for evaluating the reliability of a new energy vehicle based on vehicle networking data provided by the present invention is shown;

[0053] Figure 3 A schematic diagram of segment division of a method for evaluating the reliability of a new energy vehicle based on vehicle networking data provided by the present invention is shown;

[0054] Figure 4 Another schematic flowchart of a method for evaluating the reliability of a new energy vehicle based on vehicle networking data provided by the present invention is shown;

[0055] Figure 5 A second scoring schematic diagram of a method for evaluating the reliability of a new energy vehicle based on vehicle networking data provided by the present invention is shown;

[0056] Figure 6Shows a schematic structural diagram of a new energy vehicle reliability evaluation device based on vehicle networking data provided by the present invention;

[0057] Figure 7 Shows a schematic structural diagram of a vehicle provided by the present invention. Detailed implementation manners

[0058] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0059] In the related art, reliability assessment is mainly carried out through the fault problems pointed to by maintenance data. However, the logic for triggering faults by each vehicle enterprise is self-defined, and there will be differences in the definition of the fault triggering threshold conditions and the duration, which will lead to different reliability evaluation criteria for each vehicle model. At the same time, the fault information collected during maintenance may be different from the fault causes when the fault phenomenon occurs, and it cannot be well associated with the fault phenomenon. In addition, there are problems with the rationality and accuracy of reliability assessment only from the perspective of the faults reported by the vehicle.

[0060] Based on this, the embodiments of the present invention provide a new energy vehicle reliability evaluation method based on vehicle networking data. By obtaining the vehicle networking data of each new energy vehicle, historical charging segment data and historical driving segment data of the target vehicle model are obtained based on the vehicle networking data; by calculating the battery system reliability score of the target vehicle model based on the historical charging segment data; by calculating the drive system reliability score of the target vehicle model based on the historical driving segment data; by calculating the electrical system reliability score of the target vehicle model based on the historical charging segment data and the historical driving segment data, the reliability of the target vehicle model is evaluated based on the battery system reliability score, the drive system reliability score and the electrical system reliability score, thereby improving the rationality and accuracy of the reliability evaluation of new energy vehicles.

[0061] Figure 1 Shows a flowchart of the first embodiment of a new energy vehicle reliability evaluation method based on vehicle networking data of the present invention, and this method is executed by the vehicle. As Figure 1 shown, this method includes the following steps:

[0062] Step 110, obtain the vehicle networking data of each new energy vehicle, and obtain the historical charging segment data and the historical driving segment data of the target vehicle model based on the vehicle networking data.

[0063] Among them, the above-mentioned vehicle networking data includes, but is not limited to, vehicle charging records, driving trajectories, speed changes, battery status, motor status, fault diagnostic codes, etc. These data are collected and transmitted to the cloud server in real time through in-vehicle sensors, Controller Area Network (CAN bus), and vehicle networking communication technologies. In order to ensure the accuracy and integrity of the data, during the data acquisition process, it is also necessary to preprocess the data, including steps such as data cleaning, denoising, and format conversion, for subsequent analysis and processing.

[0064] In an alternative embodiment, when obtaining the historical charging segment data and historical driving segment data of the target vehicle model based on the vehicle networking data, data analysis can be performed on the vehicle networking data to obtain the vehicle status signal of the target vehicle model; based on the vehicle status signal, determine the charging status of each new energy vehicle in the target vehicle model, where the charging status includes the parked charging status and the non-parked charging status; based on the adjacent relationship between the parked charging status and the non-parked charging status, obtain the historical charging segment data and historical driving segment data of the target vehicle model.

[0065] Specifically, please refer to Figure 2 , the above-mentioned vehicle status signal (vehicle charging status) includes parked charging, driving charging, non-charged status, and charging completed. By dividing the charging segments and driving segments according to vehicle status signals such as parked charging, driving charging, non-charged status, and charging completed. That is, for any vehicle status signal of non-parked charging (signal≠1), the next frame of vehicle status signal that is parked charging (signal = 1) in sequence with it is used as the start frame, and continue to read. The next frame of vehicle charging status signal of non-parked charging is used as the end frame. The data stage between the start frame and the end frame is used as the charging segment, and other data stages are used as the driving segment.

[0066] Step 120, calculate the reliability score of the battery system of the target vehicle model based on the historical charging segment data.

[0067] Among them, the reliability score of the battery system of the above-mentioned target vehicle model can be obtained through statistical analysis of the historical charging segment data, including, but not limited to, indicators such as charging duration, charging times, charging success rate, and charging interruption rate. By comprehensively considering these indicators, a battery system reliability score can be assigned to each target vehicle model. This score can intuitively reflect the stability and reliability of the battery system of this vehicle model. The higher the score, the better the performance of the battery system of this vehicle model during historical charging, and it has higher reliability. Thus, it provides an important basis for the reliability evaluation of new energy vehicles, and helps consumers, manufacturers, and policymakers to more comprehensively understand the battery system performance of new energy vehicles.

[0068] In an alternative embodiment, when calculating the battery system reliability score of a target vehicle model based on historical charging segment data, the maximum temperature difference of each cell and the charge state error of each new energy vehicle can be calculated based on the historical charging segment data; the temperature difference score corresponding to the maximum temperature difference of each cell and the charge error score corresponding to the charge state error can be calculated based on a box plot; and the battery system reliability score of the target vehicle model can be calculated based on the temperature difference score and the charge error score.

[0069] In an alternative embodiment, when calculating the maximum temperature difference of each cell and the charge state error of each new energy vehicle based on historical charging segment data, feature analysis can be performed on the historical charging segment data to obtain the charging temperature feature and the charging attribute feature corresponding to each historical charging segment; the maximum temperature difference of each new energy vehicle can be calculated based on the maximum average temperature and the minimum average temperature in the charging temperature feature; the average charge state of each new energy vehicle segment can be calculated based on the average charging power, the rated power, and the battery health state in the charging attribute feature; and the charge state error of each new energy vehicle can be calculated based on the difference between the average charge state of the segment and the charge state of the corresponding segment. The average charging power is calculated based on the average current of the segment, the average voltage of the segment, and the segment duration.

[0070] Specifically, the calculation model for the maximum temperature difference of each cell is as follows:

[0071] X 1 =max{temp cell_diff i}

[0072] where temp cell_diff i is the difference between the maximum average temperature and the minimum average temperature, that is, the difference between the maximum average temperature and the minimum average temperature of the segment, unit: °C.

[0073] It can be understood that the thermal stability of the vehicle is reflected by evaluating the maximum temperature difference of each cell of the new energy vehicle (vehicle), providing a basis for the reliability evaluation of the new energy vehicle. Among them, the smaller the maximum temperature difference of each cell, the better the thermal stability of the vehicle.

[0074] The calculation model for the charge state error is as follows:

[0075]

[0076] where I avg i is the average current of the segment, unit: A; V avg i is the average voltage of the segment, unit: V; T avg i is the segment duration, unit: s; ΔSOC iIt is the difference between the end-of-fragment SOC and the start-of-fragment SOC, i.e., the state of health of the battery, unit: %; A 标称 is the nominal total stored power of the energy storage device, i.e., the rated power, kWh, and i is the number of new energy vehicles.

[0077] It can be understood that by evaluating the SOC error index corresponding to the vehicle charging segment to reflect the accuracy of the vehicle's SOC estimation, thereby enhancing the user's experience.

[0078] Furthermore, when calculating the temperature range score corresponding to the single-cell temperature range and the charge error score corresponding to the state-of-charge error based on the box plot, the upper and lower limits of the corresponding box plot can be determined based on the calculated upper and lower quartiles and the interquartile range of the single-cell temperature range and the state-of-charge error.

[0079] For example, for the single-cell temperature range, first take the single-cell temperature ranges of all vehicles in the current calculation period as eigenvalues, sort them according to the size of the eigenvalues, and obtain the upper and lower quartiles Q 75 、Q 25 and the median Q 50 , calculate the interquartile range IQR = Q 75 -Q 25 , the upper limit value of the box plot = Q 75 +1.5*IQR, the lower limit value of the box plot = Q 25 -1.5*IQR. Then calculate the temperature range score in the way of the box plot. For example, for eigenvalues at the upper limit and higher values, it is recorded as 60 points, for eigenvalues at the lower limit and lower values, it is recorded as 100 points, and the median is recorded as 80 points; for the median ≤ eigenvalue ≤ upper limit value: score = (eigenvalue - median) × (60 - median score) / (upper limit value - median) + median score; for the lower limit value ≤ eigenvalue ≤ median: score = (eigenvalue - median) × (100 - median score) / (lower limit value - median) + median score.

[0080] For example, for the single-cell temperature range, the lower quartile is 2°C, the median is 2.5°C, the upper quartile is 3°C, the lower limit of the box plot is 0.5°C, and the upper limit of the box plot is 4.5°C. If the eigenvalue of a sample vehicle (single vehicle, new energy vehicle) is 0.2°C, then the score = 100; if the eigenvalue is 1°C, then the score = (1 - 2.5) * (100 - 80) / (0.5 - 2.5) + 80 = 95; if the eigenvalue is 2.5°C, then the score = 80; if the eigenvalue is 3.5°C, then the score = (3.5 - 2.5) * (60 - 80) / (4.5 - 2.5) + 80 = 70; if the eigenvalue is 5°C, then the score = 60.

[0081] Further, when calculating the reliability score of the battery system of the target vehicle based on the temperature range score and the charge error score, the reliability score of the battery system of a single vehicle is calculated by comprehensively considering the above two indicators (temperature range score and charge error score), and the specific weights are as follows:

[0082] X 单车，电池系统 = w d1 * X d1 + w d2 * X d2

[0083] Wherein, X d1 represents the temperature range score, and X d2 represents the charge error score. The weights w d1 and w d2 are determined by combining the severity of the hazards caused by the failure of the single-cell temperature range and the SOC estimation accuracy index (charge state error), and through expert review.

[0084] Calculate the reliability score of the battery system of this vehicle by comprehensively considering the reliability scores of the battery systems of all single vehicles under the vehicle model:

[0085]

[0086] For example: The score of the single-cell temperature range of a certain vehicle is 85 points according to the box plot, and the charge state error is 82 points according to the box plot. The expert scores are w 1 = 0.45 and w 2 = 0.55 respectively. Then the calculated reliability score of the battery system of this single vehicle = 0.45 * 85 + 0.55 * 82 = 83.35. Statistically obtain the reliability scores of the battery systems of all single vehicles under this vehicle model, and calculate the mean value to obtain the reliability score of the battery system of this vehicle model.

[0087] Step 130, calculate the reliability score of the drive system of the target vehicle based on the historical driving segment data.

[0088] As above, by calculating the reliability score of the drive system of the target vehicle based on the historical driving segment data, the comprehensiveness and accuracy of the reliability evaluation of the target vehicle are ensured.

[0089] In an alternative embodiment, when calculating the reliability score of the drive system of the target vehicle model based on historical driving segment data, the drive motor temperature and the motor controller temperature of each new energy vehicle at the target vehicle speed can be obtained based on the historical driving segment data; the proportion of the first vehicles with the drive motor temperature greater than the motor temperature threshold and the proportion of the second vehicles with the motor controller temperature greater than the controller temperature threshold within the third calculation period are statistically counted; based on the box plot, the motor temperature score corresponding to the proportion of the first vehicles and the controller temperature score corresponding to the proportion of the second vehicles are calculated, and based on the motor temperature score and the controller temperature score, the reliability score of the drive system of the target vehicle model is calculated.

[0090] In an alternative embodiment, when statistically counting the proportion of the first vehicles with the drive motor temperature greater than the motor temperature threshold and the proportion of the second vehicles with the motor controller temperature greater than the controller temperature threshold within the third calculation period, the first continuous trigger count of the drive motor temperature greater than the motor temperature threshold and the second continuous trigger count of the motor controller temperature greater than the controller temperature threshold within the third calculation period can be statistically counted; the proportion of new energy vehicles with the first continuous trigger count greater than the first trigger count threshold is obtained to get the proportion of the first vehicles; the proportion of new energy vehicles with the second continuous trigger count greater than the second trigger count threshold is obtained to get the proportion of the second vehicles.

[0091] Specifically, the model for statistically counting the proportion of the first vehicles with the drive motor temperature greater than the motor temperature threshold within the third calculation period is:

[0092]

[0093] where n is the number of vehicles with the vehicle start state and the target vehicle speed in the range of 0 - 42 km / h, and the drive motor temperature is greater than the motor temperature threshold such as 175°C for 5 consecutive frames or more; N is the total number of valid vehicles within the third calculation period, and X q1 is the proportion of the first vehicles, that is, the drive motor over-temperature index. That is, by evaluating the drive motor over-temperature index to reflect the performance of the vehicle motor, the smaller the motor over-temperature frequency, the higher the reliability of the motor operation.

[0094] Please refer to Figure 3 , when calculating the motor temperature score corresponding to the proportion of the first vehicles based on the box plot, scoring can be performed according to the vehicle model dimension. For example, calculating the proportion of vehicles with over-temperature drive motors of a certain vehicle model within the third calculation period as a characteristic value, sorting according to the size of the characteristic value, and obtaining the upper and lower quartiles Q 75 、Q 25 and the median Q 50 , calculating the interquartile range IQR = Q 75 -Q 25 、the upper limit value of the box plot = Q 75+1.5 * IQR, lower limit of box plot = Q 25 -1.5 * IQR. For eigenvalues at or above the upper limit, a score of 60 is recorded; for eigenvalues at or below the lower limit, a score of 100 is recorded; and the median is recorded as 80. For median ≤ eigenvalue ≤ upper limit: score = (eigenvalue - median) × (60 - median score) / (upper limit - median) + median score; for lower limit ≤ eigenvalue ≤ median: score = (eigenvalue - median) × (100 - median score) / (lower limit - median) + median score. By the above method, the over-temperature index score of the drive motor of the current vehicle model can be obtained, that is, the motor temperature score.

[0095] The model for statistically calculating the proportion of the second type of vehicles with the motor controller temperature greater than the controller temperature threshold within the third calculation cycle is:

[0096]

[0097] where nq is the number of vehicles with the vehicle start state and the target vehicle speed in the range of 0 - 42 km / h, and the drive motor controller temperature is greater than the controller temperature threshold, such as 95°C, and the message is continuously triggered for 5 frames or more; Nq is the total number of valid vehicles within the third calculation cycle, and X q2 is the proportion of the second type of vehicles. That is, by evaluating the over-temperature index of the drive motor controller, the performance of the vehicle motor is reflected. The smaller the over-temperature frequency of the motor controller, the higher the reliability of the motor controller operation.

[0098] When calculating the controller temperature score corresponding to the proportion of the second type of vehicles based on the box plot, scoring can be performed according to the vehicle model dimension. For example, calculate the proportion of over-temperature vehicles of the drive motor of a certain vehicle model within the third calculation cycle as the eigenvalue, sort according to the size of the eigenvalue, and obtain the upper and lower quartiles Q 75 、Q 25 and the median Q 50 , calculate the interquartile range IQR = Q 75 -Q 25 、upper limit of box plot = Q 75 +1.5 * IQR, lower limit of box plot = Q 25 -1.5 * IQR. For eigenvalues at or above the upper limit, a score of 60 is recorded; for eigenvalues at or below the lower limit, a score of 100 is recorded; and the median is recorded as 80. For median ≤ eigenvalue ≤ upper limit: score = (eigenvalue - median) × (60 - median score) / (upper limit - median) + median score; for lower limit ≤ eigenvalue ≤ median: score = (eigenvalue - median) × (100 - median score) / (lower limit - median) + median score. By the above method, the over-temperature index score of the drive motor controller of the current vehicle model can be obtained, that is, the controller temperature score.

[0099] Further, based on the motor temperature score and the controller temperature score, calculate the drive system reliability score X of the target vehicle model 驱动电机 The model is as follows:

[0100] X 驱动电机 = w q1 *X q1 + w q2 *X q2

[0101] Wherein, X q1 is the motor temperature score, X q2 is the controller temperature score, and the weights w q1 , w q2 are determined according to the historical alarm data of the vehicle by respectively counting the number of over-temperature alarms of the drive motor n q1 , n q2 of the drive motor controller over-temperature alarm, and normalizing to determine the weights of the two sub-indices

[0102] Step 140: Calculate the electrical system reliability score of the target vehicle model based on the historical charging segment data and the historical driving segment data

[0103] As above, by calculating the electrical system reliability score of the target vehicle model based on the historical charging segment data and the historical driving segment data, the comprehensiveness and accuracy of the reliability evaluation of the target vehicle model are ensured

[0104] In an alternative embodiment, when calculating the electrical system reliability score of the target vehicle model based on the historical charging segment data and the historical driving segment data, the insulation resistance values of each new energy vehicle within the fourth calculation period can be obtained based on the historical charging segment data and the historical driving segment data; the proportion of the third vehicles with insulation resistance values less than the insulation trigger threshold within the fourth calculation period is statistically counted; and the electrical system reliability score corresponding to the proportion of the third vehicles is calculated based on the box plot

[0105] In an alternative embodiment, when statistically counting the proportion of the third vehicles with insulation resistance values less than the insulation trigger threshold within the fourth calculation period, the number of consecutive third triggers with insulation resistance values less than the insulation trigger threshold within the fourth calculation period can be statistically counted; and the proportion of new energy vehicles with the number of consecutive third triggers greater than the third trigger threshold is obtained to obtain the proportion of the third vehicles

[0106] Specifically, the model for statistically counting the proportion of the third vehicles with insulation resistance values less than the insulation trigger threshold within the fourth calculation period is as follows:

[0107]

[0108] Wherein, X e1The third vehicle proportion, which is the risk of too low insulation resistance value. ne is the number of vehicles with a custom-defined third-level risk of too low insulation resistance value during the fourth calculation period, and Ne is the total number of effective vehicles during the fourth calculation period. That is, by evaluating the frequency of too low insulation resistance value, the electrical reliability of the vehicle is reflected. If the overall vehicle insulation fails, it may affect the safety of the vehicle and personnel.

[0109] Table 1 Custom-defined risk of too low insulation resistance value

[0110]

[0111]

[0112] Among them, R is the insulation resistance value, unit: kΩ, V 总 is the total voltage, unit: V.

[0113] For example, when calculating the electrical system reliability score corresponding to the third vehicle proportion based on the box plot, the proportion of a certain vehicle model with a third-level risk of too low insulation resistance value in this fourth period can be calculated as a characteristic value, sorted according to the size of the characteristic value, and the upper and lower quartiles Q 75 、Q 25 and the median Q 50 are obtained. Calculate the interquartile range IQR = Q 75 -Q 25 、the upper limit value of the box plot = Q 75 +1.5*IQR, the lower limit value of the box plot = Q 25 -1.5*IQR. For the upper limit and higher values of the characteristic value, it is recorded as 60 points, for the lower limit and lower values of the characteristic value, it is recorded as 100 points, and the median is recorded as 80 points. For the median ≤ characteristic value ≤ upper limit value: score = (characteristic value - median) × (60 - median score) / (upper limit value - median) + median score; for the lower limit value ≤ characteristic value ≤ median: score = (characteristic value - median) × (100 - median score) / (lower limit value - median) + median score. Through the above scoring method, the risk index score of too low insulation resistance value of the current vehicle model, that is, the electrical system reliability score, can be obtained.

[0114] Step 150, based on the battery system reliability score, drive system reliability score, and electrical system reliability score, evaluate the reliability of the target vehicle model.

[0115] As above, by evaluating the reliability of the target vehicle model based on the battery system reliability score, drive system reliability score, and electrical system reliability score, the rationality and accuracy of the reliability evaluation of new energy vehicles can be improved.

[0116] In an alternative embodiment, when evaluating the reliability of a target vehicle model based on the reliability scores of the battery system, drive system, and electrical system, historical alarm data of the target vehicle model can be obtained based on vehicle networking data; based on the historical alarm data, the number of battery alarms of the battery system, the number of drive alarms of the drive system, and the number of electrical alarms of the electrical system are counted; the number of battery alarms, drive alarms, and electrical alarms are normalized to obtain a normalization result; based on the normalization result, the reliability scores of the battery system, drive system, and electrical system, the reliability of the target vehicle model is evaluated.

[0117] Specifically, the model for evaluating the reliability of the target vehicle model based on the normalization result, the reliability scores of the battery system, drive system, and electrical system is:

[0118] X 可靠性 = w k1 *X 电池系统 + w k2 *X 驱动电机 + w k3 *X 电气系统

[0119] Wherein, the weights w k1 、w k2 、w k3 are based on the vehicle historical alarm data, and the number of alarm times n k1 、n k2 、n k3 of the battery system, drive motor and controller, and vehicle electrical system are respectively counted, and the weights of the three modules are determined after normalization

[0120] The new energy vehicle reliability evaluation method based on vehicle networking data in the embodiments of the present invention obtains the vehicle networking data of each new energy vehicle, and based on the vehicle networking data, obtains the historical charging segment data and historical driving segment data of the target vehicle model; based on the historical charging segment data, calculates the reliability score of the battery system of the target vehicle model; based on the historical driving segment data, calculates the reliability score of the drive system of the target vehicle model; based on the historical charging segment data and historical driving segment data, calculates the reliability score of the electrical system of the target vehicle model; based on the reliability scores of the battery system, drive system, and electrical system, evaluates the reliability of the target vehicle model, thereby improving the rationality and accuracy of the new energy vehicle reliability evaluation, helping vehicle manufacturers discover the design short - comings of their own new energy vehicles, providing a direction for technical improvement, and thus promoting the healthy and rapid development of new energy vehicles.

[0121] Figure 4The flowchart of another embodiment of the new energy vehicle reliability evaluation method based on vehicle networking data according to the present invention is shown, and this method is executed by a vehicle. As Figure 4 shown, this method includes the following steps:

[0122] Step 410, obtain the vehicle networking data of each new energy vehicle, and obtain the historical charging segment data and historical driving segment data of the target model based on the vehicle networking data.

[0123] For details, please refer to Figure 1 step 110 of the embodiment shown, which will not be elaborated here.

[0124] Step 420, calculate the reliability score of the battery system of the target model based on the historical charging segment data.

[0125] Specifically, the above step 420 includes:

[0126] Step 4201, calculate the first single-cell temperature cycle range and the first state of charge cycle error of each new energy vehicle within the first calculation cycle based on the historical charging segment data.

[0127] Among them, the first single-cell temperature cycle range refers to the difference between the maximum value and the minimum value of the battery single-cell temperature of each new energy vehicle within the first calculation cycle. The first state of charge cycle error refers to the deviation between the predicted value and the actual value of the battery state of charge of each new energy vehicle within the first calculation cycle. By calculating and analyzing these data, the battery state changes of new energy vehicles during charging can be further understood, thus providing more accurate data support for subsequent reliability evaluation.

[0128] Step 4202, calculate the first temperature cycle range score corresponding to the first single-cell temperature cycle range and the first charge error cycle score corresponding to the first state of charge cycle error based on the box plot.

[0129] Among them, the box plot is a statistical chart used to display the dispersion of data, named because of its shape like a box. It can display the maximum value, minimum value, median, first quartile, and third quartile of a set of data. By using the box plot, outliers in the data can be visually identified, and the skewness and tail weight of the data can be preliminarily judged. In this step, by using the box plot to calculate the first single-cell temperature cycle range and the first state of charge cycle error, the corresponding first temperature cycle range score and first charge error cycle score are obtained. These two scores can reflect the stability of the battery temperature and state of charge of new energy vehicles during charging, thus providing a quantitative basis for subsequent reliability evaluation.

[0130] Step 4203: Calculate the second temperature cycle range score and the second charge error cycle score of each new energy vehicle in the second calculation cycle based on the historical charging segment data and the first charge error cycle score.

[0131] Specifically, data preprocessing can be performed first based on the historical charging segment data, including information such as the charging start time, end time, charging amount, and the corresponding temperature and charge status. Then, combined with the first charge error cycle score, which reflects the stability of the charge status of new energy vehicles in the first calculation cycle, these data are further used to analyze the performance of new energy vehicles in the second calculation cycle. The specific method is to calculate the range of the temperature data in the second calculation cycle to obtain the second temperature cycle range score, which can measure the fluctuation of the battery temperature in this cycle; at the same time, statistical analysis is performed on the charge error data to calculate the second charge error cycle score, which reflects the error change of the charge status in this cycle. Through these two scores, we can more comprehensively understand the stability of the battery temperature and charge status of new energy vehicles in consecutive charging cycles, providing a more detailed and in-depth quantitative basis for subsequent reliability evaluation.

[0132] Step 4204: Calculate the reliability score of the battery system of the target model based on the first temperature cycle range score, the first charge error cycle score, the second temperature cycle range score, and the second charge error cycle score.

[0133] Among them, the above process can use weighted average or other statistical methods to ensure the reasonable weight of each score in the final reliability score. For example, different importance coefficients can be assigned to these four scores, and then the weighted average is calculated based on these coefficients and score values as the final result of the battery system reliability score. Through such a calculation method, a reliability score that comprehensively reflects the stability of the battery temperature and charge status of new energy vehicles in different charging cycles can be obtained, providing valuable reference information for automobile manufacturers and consumers.

[0134] During the implementation process, please refer to Figure 5 , and the second calculation cycle (one calculation cycle is a days) is evaluated according to the box plot scoring method in the second calculation cycle; for the second calculation cycle and above, the score of this calculation cycle is calculated based on the evaluation method in the previous calculation cycle as the benchmark.

[0135] For example, for the extreme difference in monomer temperature, the eigenvalue is calculated using the vehicle data of the previous a days within the first calculation period (the first statistical period), and the upper and lower quartiles, median, upper and lower limit values of the box plot are obtained based on the eigenvalue within the first calculation period as the basis for scoring, and the scoring is carried out in the way of calculating the score according to the box plot. Within the second calculation period (the second statistical period), the extreme difference in monomer temperature is calculated using the vehicle data of a + 1 to 2*a days, and the scoring is carried out based on the upper and lower quartiles, median, upper and lower limit values of the box plot within the first calculation period. Subsequently, the upper and lower quartiles, median, upper and lower limit values within the second calculation period are calculated as the basis for scoring the box plot in the third calculation period.

[0136] Step 430: Calculate the reliability score of the drive system of the target vehicle model based on the historical driving segment data.

[0137] For details, please refer to Figure 1 Step 130 of the illustrated embodiment, which will not be elaborated here.

[0138] Step 440: Calculate the reliability score of the electrical system of the target vehicle model based on the historical charging segment data and the historical driving segment data.

[0139] For details, please refer to Figure 1 Step 140 of the illustrated embodiment, which will not be elaborated here.

[0140] Step 450: Evaluate the reliability of the target vehicle model based on the battery system reliability score, the drive system reliability score, and the electrical system reliability score.

[0141] For details, please refer to Figure 1 Step 150 of the illustrated embodiment, which will not be elaborated here.

[0142] The new energy vehicle reliability evaluation method based on vehicle networking data in the embodiments of the present invention obtains the vehicle networking data of each new energy vehicle, and obtains the historical charging segment data and historical driving segment data of the target vehicle model based on the vehicle networking data; calculates the battery system reliability score of the target vehicle model based on the historical charging segment data; calculates the drive system reliability score of the target vehicle model based on the historical driving segment data; calculates the electrical system reliability score of the target vehicle model based on the historical charging segment data and the historical driving segment data; evaluates the reliability of the target vehicle model based on the battery system reliability score, the drive system reliability score, and the electrical system reliability score, thereby improving the rationality and accuracy of the new energy vehicle reliability evaluation, helping vehicle manufacturers discover the design short - comings of their own new energy vehicles, providing a direction for technical improvement, and thus promoting the healthy and rapid development of new energy vehicles.

[0143] Figure 6The structural schematic diagram of an embodiment of a reliability evaluation device for new energy vehicles based on vehicle networking data according to the present invention is shown. As Figure 6 shown, the device includes:

[0144] A data acquisition module 610, configured to acquire vehicle networking data of each new energy vehicle, and obtain historical charging segment data and historical driving segment data of a target vehicle model based on the vehicle networking data;

[0145] A battery scoring module 620, configured to calculate the reliability score of the battery system of the target vehicle model based on the historical charging segment data;

[0146] A drive scoring module 630, configured to calculate the reliability score of the drive system of the target vehicle model based on the historical driving segment data;

[0147] An electrical module evaluation module 640, configured to calculate the reliability score of the electrical system of the target vehicle model based on the historical charging segment data and the historical driving segment data;

[0148] A vehicle model evaluation module 650, configured to evaluate the reliability of the target vehicle model based on the reliability scores of the battery system, the drive system, and the electrical system.

[0149] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding method embodiment above, and will not be elaborated here.

[0150] Through the above device and its components, the technical solution provided by the embodiment of the present invention has the following advantages:

[0151] Figure 7 The structural schematic diagram of an embodiment of a vehicle provided by the present invention is shown. The specific implementation of the vehicle is not limited in the specific embodiment of the present invention. The vehicle has the above-mentioned Figure 6 shown reliability evaluation device for new energy vehicles based on vehicle networking data. The vehicle may include: a processor 702, a communication interface 704, a memory 706, and a communication bus 708.

[0152] Among them: The processor 702, the communication interface 704, and the memory 706 communicate with each other through the communication bus 708. The communication interface 704 is used for network communication with other devices such as clients or other servers. The processor 702 is used to execute the program 710, and specifically can execute the relevant steps in the above-mentioned method embodiment.

[0153] Specifically, the program 710 may include program codes, and the program codes include computer-executable instructions.

[0154] The processor 702 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. One or more processors included in the vehicle may be of the same type of processor, such as one or more CPUs; or may be of different types of processors, such as one or more CPUs and one or more ASICs.

[0155] A memory 706 for storing a program 710. The memory 706 may include high-speed RAM memory and may also include non-volatile memory, such as at least one magnetic disk memory.

[0156] Embodiments of the present invention also provide a computer-readable storage medium storing at least one executable instruction, which, when running on a vehicle / a new energy vehicle reliability evaluation device based on vehicle networking data, causes the vehicle / a new energy vehicle reliability evaluation device based on vehicle networking data to execute the new energy vehicle reliability evaluation method based on vehicle networking data in any of the above method embodiments.

[0157] Embodiments of the present invention also provide a computer program product including computer instructions for causing a computer to execute the new energy vehicle reliability evaluation method based on vehicle networking data in the first aspect or any corresponding implementation manner thereof.

[0158] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. In addition, embodiments of the present invention are not directed to any particular programming language.

[0159] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present invention may be practiced without these specific details. Similarly, in order to streamline the present invention and assist in understanding one or more of the various inventive aspects, in the description of the exemplary embodiments of the present invention above, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. Among them, the claims following the specific implementation manners are hereby expressly incorporated into the specific implementation manners, where each claim itself serves as a separate embodiment of the present invention.

[0160] Those skilled in the art can understand that the modules in the devices in the embodiments can be adaptively changed and set in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive.

[0161] It should be noted that the above embodiments illustrate the present invention rather than limit the present invention, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim listing several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A reliability evaluation method for new energy vehicles based on Internet of Vehicles data, characterized in that: The method comprises: Acquire the Internet of Vehicles data of each new energy vehicle, and obtain the historical charging segment data and historical driving segment data of the target vehicle model based on the Internet of Vehicles data; Calculating a battery system reliability score for the target vehicle model based on the historical charging segment data; Calculating a drive system reliability score of the target vehicle model based on the historical driving segment data; Calculating an electrical system reliability score of the target vehicle model based on the historical charging segment data and the historical driving segment data; The reliability of the target vehicle model is evaluated based on the battery system reliability score, the drive system reliability score and the electrical system reliability score.

2. The method according to claim 1, characterized in that The calculating the battery system reliability score of the target vehicle model based on the historical charging segment data includes: Based on the historical charging segment data, calculating the temperature extreme difference and charge state error of each of the new energy vehicles; Calculate the temperature extreme difference score corresponding to the monomer temperature extreme difference and the charge error score corresponding to the charge state error based on the box plot; Based on the temperature extreme difference score and the charge error score, a battery system reliability score of the target vehicle model is calculated.

3. The method according to claim 2, characterized in that The calculating, based on the historical charging segment data, the temperature extreme difference and charge state error of each of the new energy vehicles includes: Performing feature analysis on the historical charging segment data to obtain charging temperature features and charging attribute features corresponding to each historical charging segment; Calculating the temperature range of each cell of the new energy vehicle based on the maximum average temperature and the minimum average temperature in the charging temperature characteristics; Calculating the segment average charge state of each of the new energy vehicles based on the average charging power, rated power and battery health status in the charging attribute characteristics; Based on the difference between the segment average charge state and the corresponding segment charge state, the charge state error of each new energy vehicle is calculated.

4. The method according to claim 1, characterized in that: The calculating the battery system reliability score of the target vehicle model based on the historical charging segment data further includes: Based on the historical charging segment data, calculating a first monomer temperature cycle extreme difference and a first charge state cycle error of each of the new energy vehicles in a first calculation cycle; Calculate, based on the box plot, a first temperature cycle extreme difference score corresponding to the first monomer temperature cycle extreme difference, and a first charge error cycle score corresponding to the first charge state cycle error; Based on the historical charging segment data and the first charge error cycle score, calculating a second temperature cycle extreme difference score and a second charge error cycle score for each of the new energy vehicles in a second calculation cycle; Based on the first temperature cycle extreme difference score, the first charge error cycle score, the second temperature cycle extreme difference score and the second charge error cycle score, a battery system reliability score of the target vehicle model is calculated.

5. The method according to claim 1, characterized in that The step of calculating the drive system reliability score of the target vehicle model based on the historical driving segment data includes: Based on the historical driving segment data, obtaining the drive motor temperature and the motor controller temperature of each of the new energy vehicles at the target vehicle speed; Counting the proportion of first vehicles whose drive motor temperature is greater than a motor temperature threshold and the proportion of second vehicles whose motor controller temperature is greater than a controller temperature threshold in a third calculation cycle; Calculate the motor temperature score corresponding to the first vehicle proportion and the controller temperature score corresponding to the second vehicle proportion based on the box plot; Based on the motor temperature score and the controller temperature score, a drive system reliability score of the target vehicle model is calculated.

6. The method according to claim 5, characterized in that The counting of the first vehicle proportion in which the driving motor temperature is greater than the motor temperature threshold and the second vehicle proportion in which the motor controller temperature is greater than the controller temperature threshold during the third calculation cycle includes: Counting the first consecutive triggering times that the temperature of the drive motor is greater than the motor temperature threshold and the second consecutive triggering times that the temperature of the motor controller is greater than the controller temperature threshold in the third calculation cycle; Obtaining the proportion of the new energy vehicles whose first continuous triggering times are greater than a first triggering times threshold, to obtain the first vehicle proportion; The proportion of the new energy vehicles whose second continuous trigger times is greater than the second trigger times threshold is obtained to obtain the second vehicle proportion.

7. The method according to claim 1, characterized in that The calculating the electrical system reliability score of the target vehicle model based on the historical charging segment data and the historical driving segment data includes: Based on the historical charging segment data and the historical driving segment data, obtaining the insulation resistance value of each of the new energy vehicles in a fourth calculation cycle; Counting the proportion of third vehicles whose insulation resistance is less than the insulation trigger threshold in the fourth calculation cycle; The electrical system reliability score corresponding to the third vehicle proportion is calculated based on the box plot.

8. The method according to claim 7, characterized in that The counting of the proportion of the third vehicles whose insulation resistance is less than the insulation trigger threshold in the fourth calculation cycle includes: Counting the third consecutive triggering times in which the insulation resistance is less than the insulation triggering threshold value within the fourth calculation cycle; The proportion of the new energy vehicles whose third consecutive trigger times is greater than the third trigger times threshold is obtained to obtain the third vehicle proportion.

9. The method according to claim 1, characterized in that: The evaluating the reliability of the target vehicle model based on the battery system reliability score, the drive system reliability score and the electrical system reliability score includes: Based on the Internet of Vehicles data, obtaining historical alarm data of the target vehicle model; Based on the historical alarm data, counting the number of battery alarms of the battery system, the number of drive alarms of the drive system, and the number of electrical alarms of the electrical system; Normalizing the battery alarm times, the drive alarm times, and the electrical alarm times to obtain a normalized result; Based on the normalized result, the battery system reliability score, the drive system reliability score and the electrical system reliability score, the reliability of the target vehicle model is evaluated.

10. The method according to any one of claims 1 to 9, characterized in that: The obtaining of historical charging segment data and historical driving segment data of the target vehicle model based on the Internet of Vehicles data includes: Performing data analysis on the Internet of Vehicles data to obtain a vehicle status signal of the target vehicle model; Based on the vehicle status signal, determining the charging status of each of the new energy vehicles in the target vehicle model, wherein the charging status includes a parking charging status and a non-parking charging status; Based on the adjacent relationship between the parking charging state and the non-parking charging state, historical charging segment data and historical driving segment data of the target vehicle model are obtained.

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